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Record W4386885117 · doi:10.1109/iotm.001.2200164

SD6LoWPAN Security: Issues, Solutions, Research Challenges, and Trends

2023· article· en· W4386885117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Internet of Things Magazine · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
Keywords6LoWPANIPv6Computer scienceProtocol stackReconfigurabilityComputer networkRouting protocolIPv4Context (archaeology)Enterprise information security architectureThe InternetComputer securityRouting (electronic design automation)Wireless sensor networkTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

Internet Protocol v6 (IPv6) for low-power wireless personal area networks has been developed to facilitate and support IP stack communication over IPv6 networks. In RFC 6550, the Internet Engineering Task Force specifies the IPv6 Routing Protocol for low-power and lossy networks to promote efficient routing in 6LoWPAN. However, this technology is not mature enough to offer secure mechanisms and communications. In this context, Software-Defined Networking has been developed to provide programmability to the resource-constrained 6LoWPAN architecture creating a new paradigm called SD6LoWPAN. Moreover, researchers have proposed machine learning to provide fast reconfigurability and intelligence for SD6LoWPAN. This article aims to provide an overview pertaining to security issues in SD6LoWPAN, considering its resource, topology, and traffic. In addition, a study is presented on the SDN- and ML-based security solutions proposed in the literature. Security research challenges and trends are also put forward.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.095
GPT teacher head0.331
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it